Bag of Visual Words Model with Deep Spatial Features for Geographical Scene Classification
Joint Authors
Feng, Jiangfan
Liu, Yuanyuan
Wu, Lin
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-06-19
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
With the popular use of geotagging images, more and more research efforts have been placed on geographical scene classification.
In geographical scene classification, valid spatial feature selection can significantly boost the final performance.
Bag of visual words (BoVW) can do well in selecting feature in geographical scene classification; nevertheless, it works effectively only if the provided feature extractor is well-matched.
In this paper, we use convolutional neural networks (CNNs) for optimizing proposed feature extractor, so that it can learn more suitable visual vocabularies from the geotagging images.
Our approach achieves better performance than BoVW as a tool for geographical scene classification, respectively, in three datasets which contain a variety of scene categories.
American Psychological Association (APA)
Feng, Jiangfan& Liu, Yuanyuan& Wu, Lin. 2017. Bag of Visual Words Model with Deep Spatial Features for Geographical Scene Classification. Computational Intelligence and Neuroscience،Vol. 2017, no. 2017, pp.1-14.
https://search.emarefa.net/detail/BIM-1140988
Modern Language Association (MLA)
Feng, Jiangfan…[et al.]. Bag of Visual Words Model with Deep Spatial Features for Geographical Scene Classification. Computational Intelligence and Neuroscience No. 2017 (2017), pp.1-14.
https://search.emarefa.net/detail/BIM-1140988
American Medical Association (AMA)
Feng, Jiangfan& Liu, Yuanyuan& Wu, Lin. Bag of Visual Words Model with Deep Spatial Features for Geographical Scene Classification. Computational Intelligence and Neuroscience. 2017. Vol. 2017, no. 2017, pp.1-14.
https://search.emarefa.net/detail/BIM-1140988
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1140988